Context-aware Traffic Flow Forecasting in New Roads
- Authors
- Kim, Namhyuk; Chae, Dong Kyu; Shin, Jung Ah; Kim, Sang-Wook; Chau, Duen Horng; Park, Sunghwan
- Issue Date
- Oct-2022
- Publisher
- ACM CIKM 2022
- Keywords
- long-term traffic prediction; traffic flow forecasting
- Citation
- ACM Conference on Information and Knowledge Management, pp.4133 - 4137
- Indexed
- OTHER
- Journal Title
- ACM Conference on Information and Knowledge Management
- Start Page
- 4133
- End Page
- 4137
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188586
- DOI
- 10.1145/3511808.3557566
- Abstract
- This paper focuses on the problem of forecasting daily traffic of new roads, where very little data is available for prediction. We propose a novel prediction model based on Generative Adversarial Networks (GAN) that learns the subtle patterns of the changes in the traffic flow according to the various contextual factors. Then the trained generator makes a prediction via generating a realistic traffic flow data of a target new road given its weather and day type. Both the quantitative and qualitative results of our extensive experiments indicate the effectiveness of our method.
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